Face mask visualization for a robotic head

Bakalářská práce se zabývá využitím zpětné projekce pro vizualizací mimiky sociobota. Je navržena a implementována kalibrace obrazu k zamezení deformace a změny jasu promítaného obrazu způsobenou zakřivením projekční plochy. Identifikace deformace provedena pomocí promítané šachovnice. K aproximaci deformace použita metoda pohyblivých vážených nejmenších čtverců (MLS). Dále je navržena testovací soustava a úprava projektoru pro krátkou promítací vzdálenost.This bachelor thesis deals with face exposure visualisation using backward projection. Image calibration is implemented for correction of distorsion and brightness change caused by curved projection plane. Identification of deformation is done by projected checkerboard patern . For deformation method of Moving Least Squares (MLS) is used. Test tool is designed and projector upgrade for shorther throw distance is implemented.

Robot móvil manipulador TiaGo en un entorno hospitarlario

En este proyecto se empleará el robot TIAGo de PAL Robotics como robot asistivo en un hospital. El robot se empleará como ayuda en tareas como monitorización, dispensación de alimentación/bebidas y medicación así como mejorar la motivación de los pacientes ingresados en su rehabilitación. Para ello, se empleará ROS y Gazebo para la simulación del entorno hospitalario y el robot, así como técnicas basadas en SLAM para el guiado del robot móvil por el hospital evitando obstáculos. Además, se hará uso de otras herramientas basadas en ROS para la planificación de trayectorias y guiado como MoveIT y ROS Control

License Plate Detection using Deep Learning and Font Evaluation

License plate detection (LPD) in context is a challenging problem due to its sensitivity to environmental factors. Moreover, the chosen font type in the license plate (LP) plays a vital role in the recognition phase in computer-based studies. This work is two folded. On one hand, we propose to employ Deep Learning technique (namely, You Only Look Once (YOLO)) in the LPD. On the other hand, we propose to evaluate font characteristics in the LP context. This work uses 2 different datasets: UFPR-ALPR, and the newly created CENPARMI datasets. We propose a YOLO-based adaptive algorithm with tuned parameters to enhance its performance. In addition to report the recall ratio results, this work will conduct a detailed error analysis to provide some insights into the type of false positives. The proposed model achieved competitive recall ratio of 98.38% with a single YOLO network. Some fonts are challenging for humans to read; however, other fonts are challenging for computer systems to recognize. Here, we present 2 sets of results for font evaluation: font anatomy results, and commercial products recognition results. For anatomy results, 2 fonts are considered: Mandatory, and Driver Gothic. Moreover, we evaluate the effect of the used fonts in context for the two datasets using 2 commercial products: OpenALPR and Plate Recognizer. The font anatomy results revealed some important confusion cases and some quality features of both fonts. The obtained results show that the Driver font has no severe confusion cases in contrast to the Mandatory font